Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all in-puts (coarse-grained dependencies). Furthermore, it does not model the internal state of a module, which can change between repeated executions. In practice, however, an out-put may depend on only a small subset of the inputs (fine-grained dependencies) as well as on the internal state of the module. We present a novel provenance framework that marries database-style and workflow-style provenance, by using Pig Latin to expose the functionality of modules, thus capturing internal state and fine-grained dependencies. A critical ingredient in our solution is the use of a novel form of provenance graph that models module invocations and yields a compact representation of fine-grained workflow prove-nance. It also enables a number of novel graph transforma-tion operations, allowing to choose the desired level of gran-ularity in provenance querying (ZoomIn and ZoomOut), and supporting "what-if" workflow analytic queries. We imple-mented our approach in the Lipstick system and developed a benchmark in support of a systematic performance eval-uation. Our results demonstrate the feasibility of tracking and querying fine-grained workflow provenance.
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)